[Case Study] How Anyreach Approaches Implementation for Agentic AI

Under the Hood at AnyReach: How We Turn Raw Calls into Safely-Shipped, Data-Driven Voice Agents

Building a world-class VoiceBot isn’t just about stringing ASR, an LLM, and TTS together. At AnyReach we treat the whole lifecycle—from a customer’s first prototype request to live production traffic—as a disciplined, data-centric pipeline. Below is a consolidated look at every layer of that pipeline, weaving in our newest work on language-model datasets, automated evaluation, call re-simulation, human-in-the-loop safety nets, and the architectural rails that make it all hum.

1 · Implementation Workflow

Prototype → Evaluate → Deliver → Release → Monitor → Improve

1. Prototype & Evaluate
   Rapid builds plus a unified Assessment Infrastructure score every candidate bot on accuracy, tone, latency and more.
2. Deliver
   When metrics turn green, Implementation bundles tagged artefacts and evaluation results into a Delivery package for Engineering.
3. Release
   Engineering re-validates quality, runs scale tests, and publishes a Release to production with formal Release Notes.
4. Monitor & Improve
   Live VoiceBots feed the same assessment engine, closing a tight feedback loop so each new iteration starts smarter than the last.

2 · Language-Model Dataset Generation

Result: a living corpus that fuels prompt-tuning, supervised fine-tuning, and eval-set refreshes—all with baked-in privacy controls.

3 · Automated LLM Evaluation

All steps are live and running in production today.

4 · Scenario & Conversation Simulations

Scenario and conversation simulations are our “wind tunnel” for VoiceBots: they let us expose new prompts, policies, or models to thousands of realistic conversations—offline, overnight, and at virtually zero cost—so only the toughest, fully-vetted version ever reaches production.

4.1 Two Complementary Simulation Modes

Both modes can run in text-only (sub-second feedback loop) or voice bot-to-bot (full TTS + ASR, perfect for latency and audio-quality checks).

4.2 Simulation Workflow in Detail

1. Scenario Library Curation
   Product and QA teams maintain YAML/JSON scenario files:yamlCopyEditid: appt-cancel-spanish-vm
persona: "Spanish-speaking parent, driving, noisy background"
goal: "Cancel child’s dental appointment and confirm reschedule date"
must_test: [voicemail_detection, language_switch]
   – Every new production bug or feature request spawns a fresh scenario file so coverage keeps expanding.
2. Automatic Parameterisation
   Templates inject random but bounded variables (names, dates, policy numbers) to avoid over-fitting while staying domain-correct.
3. Simulation Engine Kick-off
   CI pipeline triggers nightly or on every pull request:
   Scenario X × Prompt Version Y × Model Z → matrix of runs executed in parallel containers.
4. Real-Time Hooks
   During voice sims we stream audio through the same telephony stack as production, so we catch synthesis glitches, VAD misfires, and end-of-utterance clipping exactly as they would happen live.
5. Metric Harvesting & Comparison
   Each run returns:
   • Turn-level scores (relevance, tone, latency)
   • Conversation-level scores (task success, containment, policy compliance)
   • Raw artifacts (audio, JSON logs) for ad-hoc triage.
     A cantera-style diff highlights regressions versus the current gold standard.
6. Fail-Fast Gates
   • Merge blocked if any critical metric dips.
   • Severity-weighted dashboard pinpoints which scenario + metric combo broke.
   • One-click repro link re-launches the failing sim locally for rapid debugging.

4.3 Putting Simulations to Work

4.4 Why It’s a Game-Changer

• Days of manual QA → minutes of automated coverage
• No telephony bills while still exercising the full audio stack
• Deterministic repro of rare field bugs—just rerun scenario #547
• Data-privacy safe: reference-free sims need zero customer recordings
• Direct feedback loop into prompt refiner: the Auto Prompt Refiner scores new wording in sims, tweaks again, and repeats until the AVM score plateaus.

(Optional) Voice Creation — Training, Cloning, and Actor Collaboration

A. Choosing the Voice

1. Business persona workshop – define brand tone (warm, authoritative, playful, etc.).
2. Actor search & interview – assess linguistic range, studio quality, and willingness to iterate (sample script, live test call).
3. Contract & milestones – fixed deliverables (clean WAVs, pick-up rounds) keep scope tight and timelines predictable.

B. Data Collection Guidelines

C. Model Building Options

All voices pass through subjective listening tests (naturalness & brand fit) and an objective MOS (Mean Opinion Score) benchmark before going live.

D. Iteration Loop with the Actor

1. Replay real test calls in working sessions.
2. Prompt surgery on the spot – split long sentences, add SSML pauses, tweak pronunciation tags.
3. Re-record only the deltas – avoids large re-takes and speeds approval.
4. Freeze version tag once the bot survives edge-case stress tests.

Outcome: a cloned or actor-recorded voice that feels human, stays on-script, and can be re-generated instantly for future content.

5 · Replay-Driven Prompt Safeguards

We routinely re-run thousands of real production calls through current-vs-candidate prompts, gate new versions behind automated scores and human spot-checks, then shadow them on live traffic before full rollout. Safety and speed, no compromise.

1. Snapshot Live Traffic
   Every 24 h we sample fresh production calls (balanced by scenario and language) and convert them to the chat schema above.
2. A/B Simulation Runs
   Current prompt vs. candidate prompt each replay the identical caller turns in a sandbox. No telephony costs, no customer risk.
3. Multi-Layer Scoring
   LLM judge returns granular metrics (clinical safety, policy hits, empathy, task success). Separate rule-checker hunts for hard violations (PHI leak, profanity, regulatory wording).
4. Auto-Rollback
   If any red flag (e.g., emergency triage error) breaches thresholds, traffic snaps back to the last stable prompt in under two minutes.

Gate Criteria

Net effect: weekly prompt refreshes with zero production surprises.

6 · Human-in-the-Loop Transfers

Outcome: callers never get stuck, while transfer logs become a goldmine for expanding bot coverage.

• Seamless cold or warm hand-offs in < 2 s.
• Full context stitched through to the human agent and logged for training.
• Post-transfer CSAT ≥ 4.5/5 and 99.9 % transfer success.

7 · Quality Monitoring & Reporting

Every call is auto-scored on — grounding, relevance, empathy, latency, task completion, business KPIs — and surfaced in dashboards with playable audio for rapid audits.

AnyReach tracks a balanced scorecard that blends technical precision, conversational finesse, and business impact. Every production call is automatically scored, stored, and surfaced in dashboards—so stakeholders can trace improvements straight to ROI.

Assessment pipeline

1. Raw call ingestion → transcripts, embeddings, and acoustic features land in our analytics store.
2. Metric engines (LLM graders, signal-processing jobs, rule checks) score each dimension.
3. Edge scoring compares automated grades to periodic human labels, maintaining ground truth.
4. Dashboards & alerts give teams real-time views and weekly roll-ups (accuracy deltas, emerging issues, A/B test winners).

The same framework scales from a single dental-appointment bot to an enterprise contact-centre fleet—all with zero manual spreadsheet work.

8 · Voice Activity Detection & Turn-Taking

Domain-specific VAD and turn-taking models reduce double-talk events by 90 % while keeping bots responsive in noisy environments.

Evaluation Loop
Turn-taking accuracy is scored nightly: we label 1 % of traffic for true speaker boundaries and measure precision/recall. If recall dips < 95 %, the model retrains on the latest dual-channel calls—keeping behaviour aligned with real-world acoustics.

9 · Capability-to-Use-Case Map (Why It Matters to You)

Below is a customer-centric view of our live capability set. Match your business goal on the left to the capabilities on the right and you’ll see we’ve got you covered.

Legend – capability IDs reference the full list:

1 Call-to-Prompt · 2 Multilingual Dataset Generation · 3 Post-Call Analysis · 4 Pre-Call Variables · 5 Scenario Generation · 6 Voice Simulation · 7 Dashboards & Monitoring · 8 Auto Prompt Refiner · 9 AVM Score · 10 Call Re-Sim · 11 HITL Transfers · 12 Voicemail Detection · 13 Zero-Shot Voice Clone · 14 Fine-Tuned TTS · 15 Back-Channeling · 16 RouterLLM · 17 Custom LLM Fine-Tuning · 18 Turn-Taking Eval · 19 Noise-Robust VAD · 20 Web-Agent Handoffs

10 · High-Level Architecture (Tool-Agnostic View)

• Telephony Gateway – Manages inbound/outbound call streams.
• Real-Time Audio Pipeline – Speech-to-Text → LLM orchestration → Text-to-Speech on dedicated compute.
• APIs & Event Bus – Orchestrates logging, scoring, and external system integrations.
• Storage – Secure object and relational stores for audio, transcripts, metrics.
• Monitoring & Alerting – Full-stack observability wrapped in SLAs.

All components are fully HIPAA compliant and horizontally scalable.

Key Takeaways

1. Data-driven from day one – Every call fuels training, evaluation and optimisation.
2. Automation + Human Judgment – LLM judges handle scale, humans handle nuance.
3. Safety Nets Everywhere – Replay-based gating and HITL transfers keep risk near zero.
4. Capability Breadth – From zero-shot voice cloning to turn-taking analytics, the platform spans the majority of business use cases out-of-the-box.